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We'll go ahead and get our hands dirty with some C coding. DatabaseUtility Class To make life easier, we'll first create a Database utility class. Right click the project in Solution Explorer and select Class. Name the class DatabaseUtility. Replace the entire content of DatabaseUtility class with the following: C using System. SQLite; using System. Exists DataSource File.
Dispose ; Connection. We'll need it later on in our project. The Initialize method helps us create dummy entries for our employees Database. These functions will be used by our Bot when certain intents are invoked. You don't have to worry about the code at the moment.
It will all make sense later on in the article. Add the following references: C using System; using System. Data; using System. Windows; using System. Input; using Syn. Oscova; using Syn. Collection; using Syn. Entities; Add the following properties to the MainwWindow class. Add DatabaseUtility ; Bot. Firstly, we assign Bot property with an instantiated OscovaBot object.
Then, we create a new instance of DatabaseUtility class and call the Initialize method. You may ignore the UpdateDataGrid method for now. Because we'll be needing the DatabaseUtility throughout the chat session, we next add it to the MainUser's SharedData. Next, we make use of the overloaded CreateRecognizer method to create a recognizer for specific words like change, update and so on. Finally, we call the Train method.
Note: Oscova requires that the Train method is called once after components have been added, created or modified. Armed with this, we go ahead and fix the UpdateDataGrid function. Add the following code to the MainWindow class. Connection ; dataAdapter. Fill dataSet ; if dataSet. You might have already noticed DatabaseUtility making use of this method. Creating Custom Entity Recognizers So far so good. It's time to create a few entity recognizers. An Entity Recognizer is simply an implementation of IEntityRecognizer interface that reads the normalized user input and returns a collection of matching items.
For example, a number recognizer will probably use regular expression and return all the matched numbers as a collection of entities. So long story short, every item in our database is to be treated as an individual entity. This allows user inputs to have entities in them and in turn lets us create better expressions. Employee Name Recognizer Instead of creating an entity recognizer from scratch, we'll instead create a private method that leverages on the CreateRecognizer overloaded method whose second parameter takes a function that returns a collection of entities, i.
ExecuteReader ; while reader. IndexOf name, StringComparison. OrdinalIgnoreCase ; if wordIndex! The Name is the entity-type name we wish to use to refer to employee names in our database. The requestText holds the normalized value of user's request text. The normalized value is usually the same user input string but with filters applied. Discussing more on this is beyond the scope of the article.
I recommend going through the API documentations for clarifications. Using the DatabaseUtility class, we iterate through all the items in the name column of our database. We then use the IndexOf method to see if the value exists in user input. If the index value returned is anything but -1, then the word was found. When a name is found, we create a new entity of type name and set the Value and the Index property. Note: In Oscova, all entities must specify the starting index of the first occurance of matched value.
However, unlike the above Entity Recognizer, here we just create an empty recognizer and fill in the Entries property with the values of the Role column. CreateRecognizer "Role" ; DatabaseUtility. ToString ; if parser. Contains roleTitle continue; parser. Now let's call the methods from within the MainWindow constructor. Click and InputBox. KeyDown event. Evaluate message ; ResultBox. Serialize ; result.
Invoke ; InputBox. Text value is empty or not. If empty, then the clear command is sent for evaluation. After the evaluation result is returned, we set ResultBox. Text property to hold the serialized JSON value representing the evaluation result and later call the Invoke method to execute the highest scoring intent. Creating Dialogs Finally! Till now, we've been just setting up the atmosphere for our database Bot.
Now hop in and let's start our implementation of a basic dialog class. A Dialog is nothing but a collection of related intents grouped together. Now what is an Intent you ask? An Intent is an Intent! Alright that may have not sounded right. Let me explain a bit.
An Intent is what maps user input to executable actions.. So simply put an Intent, in Oscova, is a method that is invoked when a user input matches an expression. An Expression on the other hand is a pattern or example, depending on how you write it, that resembles a user input.
Oscova's scoring system compares user input to all expressions. High scoring expressions along with their intents are then returned as part of the evaluation result. Alright code veterans, we'll now create our first dialog: Right click the project in Solution Explorer, choose Add and select New Folder.
Name the folder Dialogs. Right click the folder, choose Add and select Class Name the class DatabaseEnquiryDialog and select Add. All we want is that when the user says something like What is the age of Rick? Rick being an employee , we want the Bot to return the value of the respective Age column.
We'll call our intent PropertyEnquiry. An intent method must always have a public modifier. An intent may take 0, 1 Context or Result or 2 Context and Result parameters. An intent must have atleast 1 Expression. Introduced next. Adding Expressions to Intents A expression is added to an intent via the Expression attribute. There are two main types of expressions. Example Expressions - We just write an example input and optionally annotate entities with curly braces.
Template Expressions - We write user input but instead of annotation, we down right specify entity types instead. An example expression for What is the age of Rick? Note: The order of the entity attributes is important as the sequence of annotated words or phrases are aligned against the sequence of Entity attributes. A template expression for What is the age of Rick? The only trade off is the ease of code readability. For the sake of this article, we'll add an example expression to our PropertyEnquiry intent.
Attributes namespace. Our quants or quantitative researchers read science research papers, test models, exchange knowledge from traders, test various systems and make results. Can I show you some trading systems and a forex robot that actually works? Yes, of course. Important thing: Results of all these robots are realistic because they tested on large datasets, on unseen datasets, and not overfitted.
Robots on the internet many trading expert advisors that do not have proven scientistic testing on unseen data that many programmers sell show indicative results and false, unrealistic results. For example, if we have 3 bullish years for some stock or currency pair and create a robot that will more buy than sell, it will show great performance when we test that robot. Forex trading has various trading styles and strategies. The overall Forex feature is divided into two parts- one who wants to do trading through a manual trading system and others who want to do trading by utilizing an automated trading system.
Let focus on automated trading. Now you generally have these questions- what is an automated trading system? Is this really profitable? What are the benefits and disadvantages of this trading system? Is this procedure totally safe or not? How does the automated process work? Step 1: the Trading idea Either using experience or some basic idea or research paper, the quantitative traders test various ideas. Step 2: Testing, backtesting, or machine learning In this step, we try to optimize the model, ad new rules, change rules, improve performance.
Step 3: Coding the final version of the robot In this step, programmers will write rules in python or mql4 or mql5, or any programming language and finish the robot. It is not important whether you are an experienced or a beginner and novice in this platform. The automated trading system will guide you throughout the process.
They can give you expert advice and take real trading decisions on behalf of the traders. There are the best trading programs that are computer-based. These programs are specially designed to research and analyze the overall market of trading. This particular software helps you to understand the key trading signals.
This software also points out the fluctuation of currencies and transactions as well. This software helps you to find profitable currency pairs. This software helps you to research well before placing trades on them. If you plan to input your own trading strategy, you need to monitor the overall market strictly.
To build your own forex robot, you can use the MQL4 language in the Metatrader programming platform. The whole free course 4 hours you can watch below:? How to write expert advisor in MT4: Above is a video that explains step by step how to create your own MT4 expert advisor in MetaTrader. The advantage and disadvantage of forex robot Forex Robot is a Forex trading software. It can automate trading decisions. It is a very modern financial strategically system.
There are so many companies making and selling Forex Robots. But you will have to follow the strict rules and do proper market research to avoid any kinds of scams and all. This is the only disadvantage of Forex Robots. Forex Robots are developed and programmed by different and experienced programmers.
They are responsible for coding different and various strategies into the fully automated system to open, close, and manage the position in the Forex trading market. The Forex Robot will execute the trade based on the signals provided by traders.
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